Life Cycle of Machine Learning - utkaln/machine-learning GitHub Wiki

Step 1: Scope

• Decide objective

Step 2: Collect Data

Step 3: Train the Model

• Training
• Error Analysis
• Iterative Improvement
• Can go back to Step 2 to collect more data

Step 4: Deploy the Model

• Monitor
• Performance Tuning
• Can go back to collecting more data
• MLOps - takes care of reliability and scalability aspects of the system